The Well-Being of Migrant Hispanic American Indians in New York City: From the Rural South to the Industrial North

doi:10.21203/rs.3.rs-3593134/v1

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The Well-Being of Migrant Hispanic American Indians in New York City: From the Rural South to the Industrial North

https://doi.org/10.21203/rs.3.rs-3593134/v1

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Indigenous migrants from Latin America are a neglected group in the United States and their well-being has received poor attention in the economic literature. Little is known about the needs, financial welfare, health, or education status among Hispanic American Indian households. This study uses primary data derived from New York City pertaining to pre-Hispanic groups, a demographic cohort that presents challenges in terms of research access. National-level data usually aggregates all Spanish-speaking individuals as Hispanics and fails to acknowledge the nuanced presence of these pre-Hispanic groups. Integrating Hispanic American Indian migrants who have a language barrier and low education into labor markets remains a challenge. Thus, we examine several factors that may affect household income, such as education, acculturation, and stigma/discrimination. Using logit and random forest methodologies, we substantiate that education retains its prominence as the primary determinant of income for indigenous migrants. There is a need for culturally tailored policies to promote upward mobility of this population.

JEL: I00, I24, I31

Indigenous Migrants

Education

Household Earnings

Acculturation

Labor Market Integration

Health.

The integration of migrant workers to labor markets in developed countries is beneficial to the economic prosperity of the host country, but labor assimilation may take several decades due to the presence of heterogeneity in skills, language proficiency and education ^[1]. Forced migration is a world phenomenon of shattering proportions, and its trends include the displacement of millions of vulnerable individuals with low education and poor health transiting from the less developed south to the industrial north ^[2]. Migration is associated to a high social cost due to family disintegration, affecting mostly women and children. Hispanics have greatly grown in numbers during the past decades going from 12.5% in 2000 to 19.1% of the total US population in 2022 ^[3,4]; likewise, Mexicans account for the largest share of Hispanics in the US increasing from 7.4% in 2000 to 11.2% of the total population in 2020. California, Texas, Florida, New York, and Arizona are the states with the largest concentration of Hispanics ^[5]. Undocumented migrants are a significant source of cheap labor in the US because they are unskilled workers; furthermore, indigenous immigrants have the lowest educational levels and will likely earn low wages, even if their legal status changes. Indigenous migrants from Latin America (IMLA) are target for stigma and discrimination in the US. We hypothesize that low academic profiles, precarious health, and lack of acculturation hold IMLA behind in the socioeconomic ladder. We assume that investments in human capital for immigrant populations will contribute to develop a more competitive labor force to access quality jobs and achieve upward mobility.

Rampant poverty, environmental challenges, government corruption, lack of economic prospects and drug violence have triggered Hispanic migration to pursue a better quality of life in the rich north ^[6,7]. The current literature focuses on disparities among diverse ethno-racial communities in the US rather than disparities within communities of shared origin, emphasizing their disadvantage due to legal status ^[8]. We speculate that a sizable number of IMLA with low education have crossed the US – Mexico border and remained undocumented for long time. Migration movements of indigenous people are linked to the benefits from greater economic prospects in developed economies. Hispanics in the US are usually recognized as Spanish speakers, a characteristic that “filters down to common cultural and linguistic trait that omits individualities” ^[9]. Pre-Columbian groups live in precarious conditions in their home countries, often facing “marginalization, discrimination, violence, land dispossession and a lack of access to decent housing and public health services” ^[10]. Of the 42 million indigenous people in Latin America, Mexico, Guatemala, Peru, and Bolivia, account for more than 80 % of the total individuals. Indigenous people represent 5% of the world population but comprise 15% of the poorest; poverty is driven by inadequate access to quality education and job opportunities ^[11]. The economic literature supports a positive association between income and subjective wellbeing, particularly at lower income levels ^[12]. Living standards among IMLA are below the poverty line in the US and they experience harsh socioeconomic and health disparities. Paradoxically, their income and well-being are relatively higher compared to the initial status in their country of origin; indigenous people in Latin America live in run down houses without electricity, sanitation facilities or drinking water ^[13].

IMLA living across the US have been overlooked in the economic literature and policy analyses because census data encapsules all Spanish-speaking individuals as Hispanics, thus neglecting the presence of pre-Hispanic groups. While IMLA have been an invisible and anonymous community, their presence is significant in several states of the country with an estimated population of 400,0000 people, according to the 2000 census ^[14]. Journalistic reports from the COVID-19 epidemic showed the struggle of IMLA to access health services ^[15,16,17] and made evident their disproportionate mortality rates ^[18]. The COVID-19 epidemic magnified existing disparities among families marginalized by poverty, class, race/ethnicity, and immigration status, exposing the precarious conditions of the most vulnerable community in America. IMLA were particularly affected by this novel virus due to poor access to healthcare, low-wage employment, higher chances of living in crowded places and other social-structural factors ^[19,20]. Because of low English proficiency, acculturation, and education, indigenous migrants hold low quality jobs and are more likely to live in poverty ^[21].

Population factors influencing health in Latin America are associated with high fertility rates among women, pre-term birth, low birth weight, child malnutrition, obesity, limited access to drinking water, and lack of sanitation facilities; additionally, tobacco and alcohol use are also factors contributing to poor health ^[22,^23]. IMLA groups speaking Mixtec, Chinantec, Otomi, Nahuatl, Quiche, Quechua, and Garifuna have lived in NYC for many years facing economic hardship and social isolation ^[24]. These communities are disproportionately affected by the burden of disease due to limited access to healthcare, poverty, immigration status, stigma, and discrimination. Medical conditions such as hypertension, obesity, diabetes, asthma, and kidney disease are significantly prevalent in the Hispanic community ^[25]. Health disparities among Hispanics ^[26]. Pre-Columbian migrants in NYC experience health disadvantages when compared to any other racial or ethnic group, which are amplified by psychosocial factors like problems such as depression and substance use. Besides, medical personnel consciously or unconsciously categorize indigenous and Hispanic patients as non-compliant, having language barriers, lack of health literacy and being risk takers in health decisions ^[27].

Years of schooling in Latin America are lower than those in industrial countries. Education is deficient in rural communities and vulnerable areas as well as for those facing poverty, disability, violence, and gender discrimination ^[28]. Latin American countries have traditionally scored way below industrial countries in reading, mathematics, and sciences ^[29]. Inequalities in education are associated to volatile macroeconomic conditions and the lack of investments in public education since the 1980, which enhanced existing disparities in the social fabric ^[30]. Steep differences persist between indigenous and non-indigenous people regarding income levels, living conditions, health, schooling, and access to quality jobs in Latin America. The mean years of schooling in Latin America is 9 years, which is higher than the mean for indigenous groups; for instance, Bolivia’s non-indigenous have 10 years of schooling compared to 6 for indigenous and Guatemala’s non-indigenous have 6 years of schooling versus 3 years for indigenous ^[31, ^32]. Only 27% of indigenous children are expected to complete high school and the overall literacy rate among indigenous people is 44% in Mexico ^[33].

Differentials in education amplify income disparities affecting indigenous communities in the US, making upward mobility a challenging task ^[34]. Due to their legal status, a sizable number of IMLA are more likely to live in poverty due to barriers to education, health services and social programs. For instance, first cohort of original people migrating from Mexico have an average of 4 years of schooling compared to first generation of indigenous migrants, those born in the US or arrived at an early age, who are likely to complete eight years of schooling ^[35]. Worldwide, Mexico has the one of the lowest population proportion with tertiary education and is ranked 71 in educational attainments with only 8.8 years of schooling ^[36]. Less educated IMLA are at a disadvantage in labor markets compared to any other foreign-born worker.

The sample was collected from 121 adults (18 years and older) self-identified as members of a Latin American indigenous community with a heritage of ancient tongues that existed long before the Spanish conquered the Americas. These groups of original people are concentrated in neglected districts of Brooklyn, Queens, Staten Island and Bronx (Figure 1). Structured interviews in Spanish helped assess self-reported health, education, and earnings, controlling for acculturation, English proficiency, stigma, discrimination, and depression. Because IMLA is a hard-to-reach population that has usually been invisible to most researchers, we used non-random ways to select a group of people to participate in this study. This non-probabilistic (convenience) sample has a high degree of bias ^[37] but is a cost-effective method of testing the hypothesis and is often used during preliminary research efforts to obtain a gross estimate of the results, without incurring the cost or time required to select a scientific random sample ^[38]. Our sample is not representative of the population; we are aware of limitations and factors affecting the confidence interval. We used binary variables such gender, and nativity to allow for the possibility of systematic differences. Many papers support concerning variations in unemployment rates based in gender within immigrant communities ^[39]. Institutional Review Board approval for research with human subjects (#2022-0749-Lehman) from the City University of New York was obtained. Participants signed the informed consent form and received a gift card for their time and effort. Recruitment took place at community-based organizations serving Hispanic migrants in NYC such as the Network of Transnational People in the Bronx, Sisa Pakari Group in Queens and a Brooklyn Mixtec cultural group.

FIGURE 1

Hispanic American Indians are a vulnerable community in the US and very little is known about the first comers and first-generation of migrants. Table 1 shows basic statistics for IMLA, illustrating that 67.7% (SE=4.3%) of the participants in the survey were females with a sizable number of women being the household head. Income was low across the sample; we dichotomized this variable as less than $25,000 and $25,000 or more with a staggering 71.2% (SE=4.1%) of households reporting annual earnings below $25,000. Our sample also reported that 64.5% of these families have two or more kids and that 80% of the participants were Catholics. Seemingly, high fertility rates are associated to low income and with high participation of women as head of the household. As per marital status, 62.1% (SE=4.4%) reported living in partnership or being married. The contestants had a mean age of 39.7 years (SD=10.8 years), being the youngest 20 and the oldest 68 years old. Considering their ethnic background, 85.1% came from Central Mexico, 12.4% were Garifuna women from Central Americans and 2.5% were South Americans. Among eight categories of diseases listed in our questionnaire, 83.5% had no more than one chronic medical condition.

TABLE 1

Using the Center for Epidemiological Studies-Depression survey (CESD-10), 30.6% (SE=4.1%) were considered moderately or severely depressed negatively impacting their level of daily functioning; depression among Hispanics is often misdiagnosed due to cultural, language and health literacy barriers ^[40,^41]. The use of alcohol and drugs (licit and illicit) was negligible in this sample. Participants’ years of schooling were quite low compared to the US mean of 13.7 years; in our sample, 58.7% had 9 or less years of schooling and the cumulative share of individuals with 12 years or less was 86.8%. Only 13.2% had college education but expressed concerns about finding work, which is likely related to their immigration status. One third of the subjects in our sample spoke a pre-Columbian language and five participants did not know how to read and write in Spanish. While one in three of the interviewed were born or arrived in the US as children or teenagers, acculturation and education levels were quite low. Almost all the participants (93.4%) use Spanish in their conversations at home, whereas 15.7% of them use English for oral communication in daily life and 33.9% speak an indigenous language (Figure 2). Bilingualism of Spanish and indigenous tongues for those raised in rural communities of Latin America as well as Spanish and English for those born or brought in at a young age were common in this study. In line with this result, population data indicates that around 94% of Hispanics and Mexicans born abroad speak Spanish at home, whereas 42% of US born Hispanics and Mexicans use English at home ^[42].

FIGURE 2

One in five participants felt discriminated against for being an immigrant, skin tone, appearance, or the way they speak. (Figure 3). Discrimination based on skin tone among Hispanics is well documented and extends to the provision of health care; as a matter of fact, being born in the US and having higher education are not protective against discrimination to access health services ^[43], which worsens for IMLA. To account for stigma and discrimination, we used the Project on Ethnicity and Race in Latin America (PERLA) color palette to capture self-reported measures of race and ethnicity, ranging from A (darkest) to K (lightest) skin pigmentation. This instrument has been used in several surveys to measure racial discrimination and attitudes ^[44, 45]. In our sample, 4% self-identified as being in the darkest category whereas 2% as being the lightest; F, G and H skin medium to lighter tones account for 77% of the participants (Figure 4). In this study, skin color varied from Black-Hispanics (Garifuna people, a mix of African and indigenous Latin Americans) to White-Hispanics (Mixtec people who are descendant of Spaniards). Skin tone is important for Spanish American because this feature is perceived as a source of discrimination and is associated with less opportunities for the darker people to get ahead economically and to access health services ^{[46, 47]}.

FIGURE 3 & FIGURE 4

By the means of a slightly adapted HCHS/SOL V2-Acculturaton survey, we captured different measures of assimilation of IMLA in our sample. These results showed that acculturation is low among our participants (Figure 5). The first five items addressed the use of language to estimate an acculturation index with a mean score of 1.65 (SD=0.12) and the following three items measure the use of media at home with a mean score of 1.95 (SD=0.01). This instrument ranges from 1=Only Spanish to 5=Only English. The last four questions of this survey evaluate ethnic preferences for social interactions, producing a mean score of 1.99 (SD=0.06), responses ranging from 1=All Hispanics to 5=All Non-Hispanics. These low acculturation scores are likely associated with the subject’s place of birth, age of arrival to the US, education, and socioeconomic status. Using the satisfaction with life survey (range 0-3), we found that most of the average scores are closed to 2, which implies that indigenous migrants in NYC are reasonably satisfied with most aspects of life in NYC. The items associated with standards of living, access to public services and housing are likely problematic because of their limited access to quality jobs, low education and legal status as immigrants in the US (Figure 6).

FIGURE 5 & FIGURE 6

To quantify the effects of educational levels, English proficiency and acculturation on income levels, we use a logistic regression as the baseline model. From our sample responses regarding the income level, only 28% had an income above $25,000, as indicated in Table 1. We explored different empirical approaches for identifying the strongest factors that help IMLA to move upward and avoid poverty. Many studies have used logit models along with other machine learning approaches for predicting income ^[48]. We do not consider a quantile regression because there are no IMLA households with high income in our sample ^[49]. We considered as poverty line the 20% of the lowest area median income for New York City as defined by the US Department of Housing and Urban Development for a household with at least one child ^[50]. We first included variables that describe the household characteristics, such as zip code, number of children, and marital status of the survey participant. We assume that the spatial income inequality of IMLA in NYC follows the same patterns as that of the general population. However, we include a dummy variable (Zip Code) that takes the value one when the survey participant lives in an area where the median income is higher than the median income in the whole population in NYC, and the percentage of poverty is lower than the percentage of poverty in the whole population in NYC. The second group of variables includes individual characteristics of the head of household, such as education, skin color, current age, age of arrival to the US, level of acculturation, and physical and mental health. In our logit regression model, the dependent variable denotes households with income above 20% of the area median income, and it is expressed as binary (when households have income $25,000 or more and when households have income less than $25,000). The baseline empirical model is the following:

The first variable in our model is a dummy regarding the household's address, as described above. The second variable is dummy, indicating whether there is a partner, and the variable children indicates whether the family is multi-child with three or more. The variable education indicates an education level of more than secondary. The variable age is categorical, showing the age group of the survey participant. Specifically, it takes the value 0 for an age between 20 and 30, 1 for more than 30 but less than 50, and 2 for an age over 50. The age of arrival shows whether the participant has arrived in the US as a child or an adult. The acculturation variable is the index described above as the average answer to all the acculturation questions regarding language, media communication, and social interactions. Health is the self-reported level of physical and mental health showing at least a health issue regarding cancer, obesity, diabetes, HIV/AIDS, hypertension, liver, or kidney disease.

The PERLA Color Palette is an 11-point chart designed to measure skin color in Latin America, emphasizing darker shades of color. Skin is a dummy variable that indicates whether the respondent reported a non-dark skin tone as characterized to similar studies about Latin Americans that utilize PERLA Color Palette ^[51]. Ethnicity is a categorical variable that indicates the ethnic background, including Mexico, Central America, or South America. The last variable is the gender of the survey participant. All the results are reported in Table 3 and shows that variables of education, skin, and age are all significant. For the robustness of our model, we tried different specifications; for example, we excluded the variable acculturation because some survey participants did not answer all 12 acculturation questions, and we wanted to increase the sample size in another specification. The variables of education, skin, and age remain significant. As we observe in Table 2, IMLA households who self-report a light skin color are seven times more likely to escape poverty (defined as less than 20% of the area median income). Moreover, an IMLA household with more than secondary education is 12 times more likely to escape poverty. Finally, IMLA who have arrived in the US as children or were born in this country are 13 times more likely to experience upward mobility and be able to escape poverty.

Although the sample size is small, we apply a random forest model to examine whether the significant variables have the highest important scores. We followed a standard approach similar to other studies ^[52], but without dividing the sample due to its small size. All the variables that we use are binary such as zip code, marital status, children, education, age older than 50, age of arrival, health, skin color, gender, ethnicity (whether the respondent is from Mexico or not). These results are presented in Figure 7. Based on the importance scores the education remains the top determinant of income. Both the logit model and random forest approach support this result, as expected. Although IMLA may not be able to find a job that is suitable with their education level, more educated households are able to make better decisions and eventually help the family to increase its income. Also, the higher the educational level the easier for IMLA to use English to communicate with others.

FIGURE 7

Breaking the cycle of poverty among IMLA is a challenging task when education and occupation usually determine socioeconomic status and predict health outcomes ^[53]. Also, the legal status of indigenous migrants is usually a significant determinant to advance in the societal ladder. Upward mobility across Hispanic American Indians who usually experience a low socioeconomic position and inadequate education requires policies to foster education at all levels, to improve language homogenization and to raise acculturation levels ^[54,55,56]. Furthering education among indigenous migrant families will increase literacy and English proficiency while reducing their propensity to speak the home country language. This is beneficial because more education means more human capital available to produce more goods and services, which improves their chances of finding well-paid jobs ^[57,58]. English language proficiency is an observable aspect of the acculturation process and a factor that would increase earnings at a later time ^[59]. However, due to limited English skills and lower wages, there is a tendency for IMLA to explore self-employment opportunities. Entrepreneurship, in this context, offers a chance for their economic progress and social integration by enabling the establishment of new businesses, creation of job opportunities, and generation of profits. ^[60,61].

The US has received the largest number of immigrants in the world, being Latin Americans more than 62 million people in 2020 ^[62,63]. Income inequality among Hispanics has increased in the past years; we can observe that white families possess a median wealth of $188,200, while Hispanic families have a median wealth of $36,100. Another interesting perspective is that on average, white families have approximately five times the wealth of their Hispanic counterparts ^[64]. IMLA in the US have low levels of education and inadequate access to health services, which contributes to wider economic and health disparities ^[65]. Furthermore, the size of unauthorized immigration is unknown, but estimates show that 22.1 million live undocumented, compared to a widely accepted number of 11.3 million people ^[66,67]. Migration inflows to the US are made of “younger average age, higher presence of married households, and lower educational levels than the overall US population” ^[68]. These unskilled migrants usually perform low wage duties that workers of the host country are unwilling to do ^[69], thus enabling policies that promote learning English and middle and higher education will be instrumental in the integration of immigrants to labor markets. Humanitarian migration (asylum and family reunification) is a historically accepted approach that may increase inequities affecting vulnerable groups across gender, age, education, income, and race/ethnicity. Likewise, merit-based immigration, law in Canada and Australia, rewards high-paying job offers, English proficiency and education, which happens to be economically efficient for the host country and the immigrant.

When considering median household income based on detailed ancestry, it is found that Mexicans and Mexican Americans are ranked 93^rd, followed by Puerto Ricans, among Hispanics in the US with the lowest earnings. ^[70]. Only 48.7% of Mexicans have at least a high school diploma, compared to 63.3% of Puerto Ricans and 68.7% of Cubans ^[71]. Table 3 depicts national and NYC employment distributions revealing that Hispanics and particularly Mexicans are underrepresented in skilled jobs (commerce, finance, STEM, legal and healthcare) but they are overrepresented in unskilled jobs (services, production, and agriculture). In our sample, IMLA is made of unskilled workers and have limited English competence, which impacts occupational choice and restrains their chances for vertical mobility. Poor language skills a low education among indigenous immigrants is costly not only for this group but also for the overall economy due to losses in productivity and as per the underutilization of the migrant labor force ^[72]. This comprehensive study also sheds light on the fact that individuals from immigrant and minority linguistic backgrounds such as IMLA, face an unfortunate reality of encountering stigma and discrimination. These challenges not only hinder their upward mobility but also add an extra burden to their journey towards achieving greater socioeconomic progress.

TABLE 3

The existing wide disparities in income, along with differential exposure to diseases and social stratification, have created a significant disadvantage for vulnerable communities across various races/ethnicities and genders in New York City. Unfortunately, these circumstances have contributed to the marginalization of indigenous migrant communities. Within this context, IMLA find themselves isolated and disorganized, facing numerous barriers that impede their full integration into labor markets and society as a whole. Recognizing the importance of education at all levels and proficiency in English, both factors play a crucial role in promoting cultural adaptation of individuals to their new environment, facilitating access to better job opportunities. However, the challenge lies in balancing the integration of IMLA into labor markets while preserving their native cultural values. There is a concern that attending the school system may result in a loss of their native identity. Moreover, the absence of culturally tailored policies to address socioeconomic disparities, coupled with the lack of education and poor language skills among IMLA communities, creates additional barriers in overcoming poverty. Efforts to uplift these communities require a holistic approach that considers their unique needs and fosters the development of tailored support systems to help them integrate successfully into society and overcome socioeconomic challenges.

Using primary data, we conducted a comprehensive analysis of demographic, epidemiological, and socioeconomic factors to gain insights into the contextual determinants of income among indigenous migrant communities residing in NYC. Our research findings not only provide a deeper understanding of their unique needs but also emphasize the urgent necessity to advocate for more inclusive policies that specifically cater to the Hispanic American Indian population. In particular, the development of culturally tailored strategies to support the most vulnerable subgroup within the Hispanic community becomes paramount. A key finding from our study, supported by both the logit model and random forest methodology, highlights the continued significance of education as the primary determinant of income for indigenous migrants. However, it is disheartening to observe that despite educational achievements, stigma and discrimination persistently undermine their progress. This underscores the importance of addressing these systemic issues to ensure a fully integrated labor force of IMLA, ultimately leading to improved standards of living for these communities.

To further advance our understanding and promote upward mobility among indigenous migrants, it is crucial to conduct more comprehensive studies focusing on their unique needs and challenges within the US. By gaining deeper insights into their experiences and circumstances, we can develop more effective strategies to support their socioeconomic progress. Lastly, it is worth noting that as English proficiency improves among indigenous migrant communities in the US, it may result in greater identification with the culture of the host country. This evolving cultural assimilation should be examined alongside strategies to preserve and celebrate their native identities, striking a balance that respects their heritage while facilitating integration into mainstream society.

Conflict of Interest: We, the authors, do not have any conflict of interest.

Grants: This study was supported by the Black, Race and Ethnic Studies Initiative (BRESI) at the Research Foundation of the City University of New York.

Author Contribution: JD: conceptualization, data curation, methodology, formal analysis, writing (original draft), data visualization. AK & TSS: data visualization, methodology, formal analysis, writing (original draft).

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Table 1) Basic Statistics: Indigenous Migrant Communities from Latin America Living in NYC

	Sample Proportion
Female	67.7%
Age (Years)	39.7
Income (Poorer)	71.2%
Languages Spoken
English	15.7%
Spanish	93.4%
Indigenous	33.9%
Children
0	22.3%
1	13.2%
2 to 4	55.4%
4+	9.1%
Catholic	81.8%
Marital	61.2%
Depression	30.6%
Age of Arrival to the US
0 to 12	15.7%
13 to 18	19.8%
19 to 28	47.9%
28 +	16.5%
Ethnicity
Mexico	85.1%
Central America	12.4%
South America	2.5%
Number of Conditions
0	54.5%
1	28.9%
2	11.6%
3	3.3%
4	1.7%
School Levels
Primary or less	24.8%
Secondary or less	33.9%
High School or less	28.1%
College	13.2%

Table 2) Baseline Model Predicting Household Income

			IMLA (n=85)
		Parameter Estimates & SE	Odd Ratio
Zip Code		-0.010 (0.738)	0.991
Marital Status		-0.162 (1.039)	0.851
Children		-1.912 (1.522)	0.148
Marital*Children		1.067 (1.720)	2.906
Education		2.457*** (0.895)	11.675
Age		2.630** (1.131)	13.879
Age of Arrival		-1.040 (2.532)	0.353
Age*Age of Arrival		-0.054 (1.230)	0.947
Acculturation		0.126 (0.591)	1.134
Health		-0.556 (0.697)	0.573
Skin Color		2.008* (1.015)	7.448
Ethnicity		-0.695 (1.156)	0.499
Gender		0.753 (1.156)	2.122
	(*) P-value < 0.01, () P-value <0.05, (*) P-value < 0.1

Table 3) Condensed Employment Distribution (National and New York City)

	United States			New York City
	Population	Mexicans	Hispanics	Population	Mexicans	Hispanics
Commerce/ Finance	37.0%	27.9%	29.3%	36.6%	22.4%	29.7%
STEM	6.0%	2.3%	2.9%	4.8%	1.8%	2.4%
Social and Legal	8.8%	5.1%	5.7%	10.4%	4.3%	7.1%
Services	27.2%	35.8%	35.7%	30.1%	51.4%	40.2%
Production/Agriculture	11.5%	22.5%	19.5%	6.8%	16.9%	11.1%
Healthcare	9.3%	6.1%	6.6%	11.2%	3.2%	9.5%
Military	0.35%	0.28%	0.32%	0.03%	0.04%	0.03%

Source: US Census 2020 and 2016-2020 5-year ACS, US Census 2010 and 2010 5-year ACS, US Census 2000 and 2000 5% ACS.

No competing interests reported.

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The Well-Being of Migrant Hispanic American Indians in New York City: From the Rural South to the Industrial North

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